Early detection of disease and malignant tissue can lead to a better prognosis. The development of non-invasive methods for detection and characterization of tumors has an extreme importance in current medicine. Magnetic resonance imaging (MRI) is a noninvasive medical test that can help physicians diagnose and treat medical conditions. MR imaging uses a powerful magnetic field, radio frequency pulses and a computer to produce detailed pictures of organs, soft tissues, bone and virtually all other internal body structures. The images can be examined on a computer monitor, printed or copied to compact disc.
By way of useful background information, according to Breast MRI: fundamentals and technical aspects, Hendrick, New York, N.Y.: Springer (2008), one effective breast MRI protocol for the detection and diagnosis of breast cancer has several essential elements: a non-fat-saturated T1-weighted pulse sequence, a fat-saturated T2-weighted pulse sequence, and a set of contrast-enhanced pulse sequences at different phases to obtain identically acquired pre-contrast and multiple post-contrast views.
From a workflow standpoint, a clinician typically views MR image datasets by loading and outputting them to a computer, for display on a computer monitor or series of computer monitors. In this manner, the physician can visually study and manipulate the various images or “softcopies” of the images. The clinician often pre-defines a hanging protocol, which refers to the clinician's preferred arrangement of images for optimal softcopy viewing. Different clinicians might prefer to review images in different manners, depending on their experience and personal preferences. Synthesizing the information across images can be a laborious process for the clinician, especially due to the large number of thin slice images provided in each MR image dataset and the amount of spatial information in one dataset that might need to be correlated with information from one or more other datasets.
Some researchers have developed fully automated multi-modal MM fusion techniques. By way of useful background information, one such example is described in INTEGRATING STRUCTURAL AND FUNCTIONAL IMAGING FOR COMPUTER ASSISTED DETECTION OF PROSTATE CANCER ON MULTI-PROTOCOL IN VIVO 3 TESLA MRI, by Viswanath et al, Proc. SPIE 7260, 726031 (2009). Disadvantageously, prior art fusion methods such as those described in Viswanath, provide preset solutions (e.g., fusion parameters) that are applied to all datasets regardless of the clinician's individual needs and desires and fail to integrate certain parameters of use in forming fusion images that aid clinician's in differentiating tissue types. These approaches, thus, lack both intuitiveness for a clinician and flexibility in adapting to a clinician's specific protocol.